Optimal placement and number of energy transmitters in wireless sensor networks for RF energy transfer
Bibliographic record
Abstract
Energy efficiency is one of the most critical design issues in wireless sensor networks (WSNs). Recently, RF energy transfer emerges as a promising solution to enhance energy efficiency. In RF energy transfer, energy is supplied to wireless networks through dedicated energy transmitters. WSNs can be equipped with the RF energy charging capabilities such that the WSNs are referred as wireless rechargeable sensor networks. This paper aims to 1) optimally place the energy transmitters and 2) determine optimal number of energy transmitters in WSNs with RF energy transfer. For optimal placement of energy transmitters, a trade-off between maximum energy charged in the network and fair distribution of energy is studied. We present a mechanism by defining a utility function to maximize both total energy charged and fairness. For optimal number of energy transmitters, an optimization problem is formulated and solved while satisfying the constraint on minimum energy charged by each sensor node. Simulation results illustrate the performance of WSNs with RF energy transfer in terms of average energy charged, fairness, and optimal number of energy transmitters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".